Complicating the processual early state

Deriving from late nineteenth-century Western thinking (Brown Reference Brown2002: 457–69), the processual model of the early state emphasised regionally organised societies composed of ruling and commoner classes, and highly centralised and internally specialised government. These were stable social and political edifices, with powerful rulers controlling quotidian affairs. Since the 1970s, archaeologists have sought to reassess this model (e.g. Johnson & Earle Reference Johnson and Earle1987: 246; Marcus & Feinman Reference Marcus, Feinman, Feinman and Marcus1998: 4). The reassessment has been twofold. First, most scholars now recognise that earlier models overstate the cohesion, nimbleness and reach of early states (Brumfiel Reference Brumfiel1992; Khatchadourian Reference Khatchadourian2016). These polities are now seen as fraught with internal strife, ever struggling to implement their limited power over surrounding communities (e.g. Richardson Reference Richardson, Ando and Richardson2017). Second, processual models are now considered as too narrow (Yoffee Reference Yoffee, Yoffee and Sherratt1993; McIntosh Reference McIntosh2005; Kenoyer Reference Kenoyer, Marcus and Sabloff2008); councils, competing clans and other social structures that were more flexible and less hierarchical could also organise large populations into state-like entities (e.g. Green Reference Green2021).

Thus, early states are now thought to have been more heterarchical than previously conceived. Unfortunately, ‘heterarchy’ is difficult to define. Is it, for example, “an organizational form of distributed intelligence” (Stark Reference Stark2009: 19), “a partially ordered level structure implicating a rampant interactional complexity” (Kontopoulos Reference Kontopolous1993: 381), or “the collective and dynamic management strategies that undergird forms of exchange” (Crumley Reference Crumley, Scott and Kosslyn2015: 12)? The term is better defined by what it is not: classic hierarchy, with a leader at the top, elites in the middle and subjects at the base.

While agreeing with many of the critiques of the processual state, we argue that alternative models of the political structure of early, state-like organisations remain poorly developed and inadequately fleshed out. In particular, most alternatives are derived from contemporary analogies or based on historically documented societies (Claessen Reference Claessen1978; Blanton & Fargher Reference Blanton and Fargher2008). The earliest state-like organisations, however, were probably more ‘heterarchical’ as their inchoate political institutions came into being. In order to explore these early political structures, archaeologists have considered a variety of data, from household assemblages (e.g. De Lucia & Overholtzer Reference De Lucia and Overholtzer2014) to indices of labour organisation (e.g. Abrams & Bolland Reference Abrams and Bolland1999). The application of SNA to iconography provides another tool for understanding these early, state-like polities.

Social network analysis and archaeology

Social network analysis is an approach that conceptualises social structure as a network of ties between entities; it focuses on the nature of the ties that binds these entities together and seeks to characterise the types of communities that these ties create (Wetherell et al. Reference Wetherell, Plakans and Wellman1994: 645). Typically, SNA uses ‘nodes’ and ‘links’ to visualise a network and then applies graph theory to interpret the nature of the community formed. Nodes are the entities being studied—most often a set of individuals—and links are the ties that bind the individuals together, such as friendships, a purchased product, or shared political affiliation.

The resulting networks of nodes and links are often visualised in sociograms of interconnected circles and lines. Circle size, colours, line thickness and other features are used to provide more visual information about the network. Further visualisation techniques help to highlight aspects of the network, and a variety of statistical methods can be applied to characterise and compare networks with rigour (Wasserman & Faust Reference Wasserman and Faust1994; Scott Reference Scott2017).

SNA has been widely applied in archaeology, especially over the last two decades (for discussions of SNA in archaeology, see Knappett Reference Knappett2013; Mills Reference Mills2017; Peeples Reference Peeples2019). Except for a few studies, such as that of Munson and Macri (Reference Munson and Macri2009), which examined elite ties using hieroglyphic evidence, the nodes used are usually settlement sites. The links between settlements tend to be imported artefacts or shared decorative elements, such as those on ceramics (e.g. Lulewicz Reference Lulewicz2019; Birch & Hart Reference Birch and Hart2021). Previous applications of SNA to early states share this focus on settlements and mutual assemblages, improving our understanding of incipient economic, political and social relationships (e.g. Kerig et al. Reference Kering2019; Vela Reference Vela2019).

Earlier SNA approaches that provide particularly nuanced insights into early state politics have made use of written records. In the context of ancient Egypt, for example, the application of SNA has illuminated the role of kinship—both real and fictive—in political organisation (Martinet Reference Martinet2013; Chollier Reference Chollier2019). The earliest states in Egypt, and elsewhere, however, emerged before the advent of writing, or generated scant records. Extant iconography, on the other hand, is often available from these early states. Here, we argue that the application of SNA to such iconographic evidence can help to reveal some of the contours of early political structures.

Wari and Middle Horizon agents

By the end of the eighth century AD, the Wari polity had started to expand out from Ayacucho, a highland valley 330km east of Lima, Peru (Figure 1). The ensuing spread of Wari-style art and architecture defines the Central Andean Middle Horizon. Huari, the polity's capital, swiftly became the largest city in the Andes—extending to as much as 10km² in area (Isbell Reference Isbell and Millones2001, Reference Isbell, Manzanilla and Chapdelaine2009a)—and was characterised by substantial variation in social statuses expressed through architecture, artefact assemblages and funerary practices (Isbell Reference Isbell2004). Evidence of investment in agricultural infrastructure and of change in regional settlement patterns have led to a scholarly consensus that Wari was one of the first states in the Andes (Isbell & Schreiber Reference Isbell and Schreiber1978; Schreiber Reference Schreiber1992).

Figure 1. Landsat image of Peru, showing the extent of Wari influence in red. The city of Huari, highlighted in yellow, and the locations in white feature agent images. No agents have yet been documented in the three Wari-affiliated settlements in black (map by the authors; base map: Google Earth).

The political organisation of the Wari polity and the mechanisms behind its spread, however, remain opaque. Although Wari authorities are often inferred, there is no clear evidence at Huari for palaces or primary temple complexes. The largest public spaces within early Middle Horizon Huari could hold just a small fraction of its population, and there is little that suggests centralised urban planning (Pérez Calderón Reference Pérez Calderón1999; Isbell Reference Isbell, Evans and Pillsbury2009b; Ochatoma Paravicino et al. Reference Ochatoma Paravicino, Romero and Mancilla Rojas2015). Instead, in the first two centuries of the Middle Horizon, Huari, along with other Wari settlements, were organised around the independently built and maintained compounds of extended households.

Judging from depictions of warriors, prisoners and human sacrifices across a variety of media that date to around AD 800, the expansion of the Wari polity was violent (Cook Reference Cook1994; Isbell Reference Isbell and Shimada2007: figs. 3.15A & B; Ochatoma Paravicino Reference Ochatoma Paravicino2007; Knobloch Reference Knobloch and Makowski2010). Dozens of outlying Wari-affiliated sites were established, and sumptuary objects, such as spondylus from Ecuador and parrot feathers from the Amazon, arrived at Huari from across the Central Andes and beyond (Jennings & Craig Reference Jennings and Craig2001; McEwan & Williams Reference McEwan, Williams and Bergh2012; Rosenfeld et al. Reference Rosenfeld, Jordan and Street2021). The nuances of Wari expansion, however, remain unknown, as do the relationships between Wari-affiliated colonists, circulating prestige goods and local populations (Jennings Reference Jennings2010).

We contend that information about this polity's expansion can be found in the examples of Wari art, which feature a repeating suite of humans with distinct costume and facial markings. Depicted primarily in ceramics (often face-necked jars, cups and serving vessels), these individuals can be distinguished from representations of deities, who are shown with crossed fangs and vertically divided eyes (Menzel Reference Menzel1964, Reference Menzel1968; Cook Reference Cook1994; Knobloch Reference Knobloch and Makowski2010). Here, we use the term ‘agent’ for these human individuals to emphasise the active role they played in shaping Middle Horizon society (Dobres & Robb Reference Dobres and Robb2000). We assume that the men and women depicted as agents in Wari art represent individuals, or are stand-ins for kinship or ethnic groups. Some agents are almost certainly Wari-state actors, while others may represent their allies or rivals from different parts of the Central Andes. Such relationships can be reconstructed from narrative scenes of cooperation or confrontation, allowing for the creation of tentative agent ‘biographies’ that hint at their stories (Knobloch Reference Knobloch and Makowski2010, Reference Knobloch2016, Reference Knobloch, Isbell, Uribe, Tiballi and Zegarra2018) (Figure 2).

Figure 2. Tapestry fragment hinting at stories about agents. Ten agents are depicted in a confrontational pose, with two large, mythical avian figures grasping Agents 100 (left) and 101 (right) by their topknots. Top row (left to right): Agents 102, 106, 158, 103, 112 and 101; middle row under feet: Agent 100 and 108; bottom row: Agents 128 and 129. Agent 100 is also shown at the wing of the left avian figure. Site E, Ocucaje, Ica Valley, Peru, dating to Middle Horizon 1B or 2A (Menzel Reference Menzel1977: fig. 130) (courtesy of the Phoebe Apperson Hearst Museum of Anthropology and Regents of the University of California Berkeley, 4-4556 (actual size: 0.925 × 0.520m)).

Reconstructing networks of Middle Horizon agents

Knobloch (Reference Knobloch2002) has created an open access, online database presenting an inventory of Middle Horizon human images that continues to be updated. Each agent is defined by physical attributes and accessories, such as tunics and shields, and then assigned a number (Figure 3). Facial elements include painted or tattooed designs, earspools, labrets, nose plugs, ear piercings and hairstyles. Headgear includes headbands, various caps, four-cornered hats with points or tassels at each corner, a conical hat with horns, a feline pelt with its head atop the agent's forehead, a ‘top hat’ form with vertical feathers, cowl-shaped hoods, and caps adorned with silver sequins (Knobloch Reference Knobloch and Makowski2010, Reference Knobloch and Bergh2012, Reference Knobloch, Isbell, Uribe, Tiballi and Zegarra2018).

Figure 3. Examples of features used to distinguish agent identities (with Agent number), such as headgear, ear plugs and facial painting. Agent 152's incomplete face (due to missing sherds) is reconstituted within the dashed line (illustrations by P. Knobloch).

To conduct our SNA, 56 agents with well-defined identities were selected from Knobloch's online database. As documented in the database, 54 come from 33 provenances listed with each agent under the heading ‘References’; 36 of these agents occur on artefacts alongside other agents (see Tables S1 and S2 in the online supplementary material (OSM)). Some artefacts come from specific locations, such as excavated sites, while others have only general locations, such as a particular river valley. Due to these limitations, we have chosen a presence/absence methodology that focuses on two questions: a) based on the presence of the same agents, which locations have more similarities? and b) among agents, which have more similar associations based on their presence together on the same artefacts?

To address these questions, Gibbon constructed two networks. The first is an agent-location network, where the nodes are the locations. The links connect locations that feature the same agent, based on the Jaccard similarity coefficient, where the shared presence of a similar suite of agents results in increased similarity (Figure 4). The second is an agent-agent network, where the nodes are agents, and the links are artefacts that feature the shared presence of one or more agents. The Jaccard coefficient is the most appropriate similarity coefficient to use for the presence/absence of archaeological data because it is not affected by the shared absence of a particular trait and therefore reduces the sampling error inherent in datasets that do not contain a representative sample (Habiba et al. Reference Habiba, Athenstädt, Mills and Brandes2018). Higher Jaccard coefficient scores indicate greater similarities between nodes that are represented by giving greater weight to links (Peeples & Roberts Reference Peeples and Roberts2013). This allows for the strength of connections between locations/agents to be included in network analysis and visualisation. Links are undirected, meaning that interactions are considered to be symmetrical or equal. Undirected links are used in this study, as the direction of interaction cannot be determined definitively.

Figure 4. Geographic (A & B) and relational (C & D) layouts of the Middle Horizon agent-location network. Relational layout repositions nodes so that the most central and connected are toward the centre of the graph. The images on the left (A & C) show the full network, while those on the right (B & D) show the 20 per cent reduction that removes the weakest links from the network (figure by E. Gibbon).

The size of nodes in the agent-location network indicates the centrality of their network position (eigencentrality), showing the connectivity of a location to other locations in the network. Larger nodes indicate greater connectivity. Likewise, the larger the node in the agent-agent network, the more often that agent is associated with other agents, based on their presence on the same artefacts. The colours in the models reflect clusters of locations or agents that share more similarity with each other than with other clusters, as determined by the Louvain community detection algorithm (Blondel et al. Reference Blondel, Guillaume, Lambiotte and Lefebvre2008). Widely used in SNA, this algorithm is similar to cluster analysis, which is commonly used in archaeology.

Analysing the agent networks

As Figure 4 illustrates, the critical features of a network can be obscured by many weak, often inconsequential, links. Therefore, three methods to elucidate the network's most critical relationships are applied: clustering coefficient, node deletion and link deletion (Barabási Reference Barabási2013). The clustering coefficient indicates how strongly nodes cluster together in a network as a measurement of the degree of interconnectedness between nodes. Tightly knit cliques are common in social networks (Watts & Strogartz Reference Watts and Strogatz1998) and are often correlated with similarity measures.

Link and node deletion are two related methods of deconstruction that identify a network's ‘core’—the integral relationships that structure the network. Link deletion removes weaker ties systematically, based on the link weight, as measured by the Jaccard similarity coefficient. This allows for the removal of links between sites or agents that share only a limited number of identical agents. In this analysis, the removal process is repeated at 20 per cent intervals until all links shared by nodes are removed and the network collapses into unconnected components (Figures 4B & 4D show the beginning of this process). By removing the ‘weak links’, those ties that are more integral to each of the Middle Horizon agent networks can more easily be identified.

The systematic removal of less-connected nodes is known as K-core deletion (Batagelj & Zaveršnik Reference Batagelj and Zaveršnik2011). This method allows an analyst to set a minimum degree threshold (the number of links any given node has) for it to remain within a given network. In the agent-location model, for example, nodes were eliminated if they had fewer than two connections (K-core 2), followed by nodes with fewer than three connections (K-core 3). This process continued until nodes with fewer than 13 connections were eliminated (K-core 13), at which point the removal of one more node would have resulted in network fragmentation (Figures 5A & 5B). Through node deletion, the most important hubs of a network are brought forward.

Figure 5. Examples of stress testing that helps to identify the most important nodes and links in the relational agent-location network: K-core tests that leave only the most connected hubs in place (K-core 5 shown in A and K-core 13 in B); complementary tests where high-degree hubs are successively removed from the network (to degree 17 shown in C and to degree 13 in D) (figure by E. Gibbon).

To complement this process, hub deletion was also conducted. In contrast to K-core deletion, hub deletion systematically removes the nodes with the largest number of connections to other locations. Hub deletion starts with the removal of the highest degree nodes in the network (in our case, those with 20 connections) and continues until the removal of nodes causes the network to collapse into multiple unconnected components (Figures 5C & 5D). This process can be used as a test for network resilience and allows evaluation of the dependency of the network on central places.

To gather more information, other measures can be used to calculate the centrality of nodes from an original network and any of the networks created by node and link deletion (for centrality measures, see the glossary in Collar et al. Reference Collar, Coward, Brughmans and Mills2015). We used degree, closeness, betweenness and eigenvector centrality measures to quantify individual node properties that may help to identify a location or agent's role within the overall network. Degree centrality is a measure of how many links a node has. Closeness centrality is the reciprocal of the shortest paths between a node and all other nodes in the network—a more ‘central’ node is closer to other nodes. Betweenness centrality is a measure of the shortest paths between each node in a network that pass through a node. Eigenvector centrality is a measure of a node's influence on the network, as calculated by the degree centralities of its connections—Google's ‘PageRank’, for example, uses this measure of centrality.

Clustering, deletion and centrality measures characterise networks, but we also need to ensure that these are meaningful patterns and not a function of chance. One thousand randomly generated networks were created with the same number of nodes and links in order to approximate a null model (Butts Reference Butts2008; Gjesfjeld & Phillips Reference Gjesfjeld, Phillips and Knappett2013). Mann-Whitney U-tests were then applied to compare the centrality measures of each of the networks with the corresponding null model to determine the statistical significance of any observed patterns in the archaeological data (see Table S3 in the OSM). The Mann-Whitney U-test was selected because it permits two independent samples to be compared without assuming a normal distribution. The statistical tests summarised in Table S3 suggest that the clustering and centrality observed in the archaeological networks were not due to chance.

Comparison of the archaeological networks with randomly generated networks, combined with node and edge deletion tests, also allows for an overall evaluation of the robustness of the archaeological data. The patterns and results that consistently appear across each iteration of the networks are unlikely to be altered significantly by the inclusion of new data, even if the details of the networks change, as inevitably they will with future discoveries.